Imagine giving an AI agent a toolbox and asking it to build a birdhouse. Current AI agents often pull out *every* tool, regardless of whether they need a hammer or a complex laser cutter. This isn't just inefficient—it's costly, especially when these tools are powerful large language models (LLMs) that charge by the word. New research introduces EcoAct, a clever way to teach AI agents to budget their resources. Instead of loading every tool into memory at the start, EcoAct gives agents a single “meta-tool”—think of it as a tool registry. The agent then has to decide which tools to “register” based on the task at hand. This is like checking the toolbox inventory before starting the project. The result? In complex tasks, EcoAct cuts computational costs by over 50% without sacrificing performance. How does this work? EcoAct leverages a simple trick: tool names. Just like a carpenter can quickly identify the right tool by its name, EcoAct lets agents decide what to use based on these concise identifiers. It only accesses the full tool details when necessary. This method also avoids overwhelming the agent with too much information, improving decision-making. While initial tests are promising, there are still challenges. For instance, letting agents register multiple tools simultaneously seems appealing but actually hurts performance—they become less discerning about what to register and sometimes make the wrong choices. Future research could explore smarter ways to balance this trade-off. EcoAct's innovation lies in its simplicity. By mimicking how humans use tools, it significantly improves the efficiency of AI agents. This could have major implications for complex AI applications where costs and efficiency are critical, like scientific research, robotics, and more.